This paper re-examines the problem of parameter estimation in Bayesian
networks with missing values and hidden variables
from the perspective of recent work in on-line learning [Kivinen & Warmuth,
1994].  We provide a unified framework
for parameter estimation that encompasses both on-line learning, where the
model is continuously adapted to new data cases as they arrive, and the more
traditional batch learning, where a pre-accumulated set of samples is used
in a one-time model selection process. In the batch case, our framework
encompasses both the gradient projection algorithm
and the EM algorithm for Bayesian networks. The
framework also leads to new on-line and batch parameter update schemes,
including a parameterized version of EM.
We provide both empirical and theoretical results indicating that
parameterized EM allows faster convergence to the maximum likelihood
parameters than does standard EM.

<p>